ReXNet-1.3x model
Pretrained on a dataset for wildfire binary classification (soon to be shared). The ReXNet architecture was introduced in this paper.
Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redundancy.
Installation
Prerequisites
Python 3.6 (or higher) and pip/conda are required to install PyroVision.
Latest stable release
You can install the last stable release of the package using pypi as follows:
pip install pyrovision
or using conda:
conda install -c pyronear pyrovision
Developer mode
Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source (install Git first):
git clone https://github.com/pyronear/pyro-vision.git
pip install -e pyro-vision/.
Usage instructions
from PIL import Image
from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
from torchvision.transforms.functional import InterpolationMode
from pyrovision.models import model_from_hf_hub
model = model_from_hf_hub("pyronear/rexnet1_3x").eval()
img = Image.open(path_to_an_image).convert("RGB")
# Preprocessing
config = model.default_cfg
transform = Compose([
Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
PILToTensor(),
ConvertImageDtype(torch.float32),
Normalize(config['mean'], config['std'])
])
input_tensor = transform(img).unsqueeze(0)
# Inference
with torch.inference_mode():
output = model(input_tensor)
probs = output.squeeze(0).softmax(dim=0)
Citation
Original paper
@article{DBLP:journals/corr/abs-2007-00992,
author = {Dongyoon Han and
Sangdoo Yun and
Byeongho Heo and
Young Joon Yoo},
title = {ReXNet: Diminishing Representational Bottleneck on Convolutional Neural
Network},
journal = {CoRR},
volume = {abs/2007.00992},
year = {2020},
url = {https://arxiv.org/abs/2007.00992},
eprinttype = {arXiv},
eprint = {2007.00992},
timestamp = {Mon, 06 Jul 2020 15:26:01 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2007-00992.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Source of this implementation
@software{Fernandez_Holocron_2020,
author = {Fernandez, François-Guillaume},
month = {5},
title = {{Holocron}},
url = {https://github.com/frgfm/Holocron},
year = {2020}
}